Patent application number | Description | Published |
20080209030 | Mining Web Logs to Debug Wide-Area Connectivity Problems - Internet service providers and their clients communicate by transmitting messages across one or more networks and infrastructure components. At various points between the service provider and the clients, inclusively, records may be created of each messages occurrence and status. These records may be read and analyzed to determine the effects of the networks and infrastructure components on the provided quality of service. User-effecting incidents (e.g., failures) occurring at networks may also be identified and described. | 08-28-2008 |
20080267083 | Automatic Discovery Of Service/Host Dependencies In Computer Networks - An activity model is generated at a computer. The activity model may be generated by monitoring incoming and outgoing channels for packets for a predetermined window of time. To generate an activity model, an input and an output channel are selected. A probability distribution function describing the observed waiting time between packet arrivals on the selected input channel and the selected output channel is generated by mining the data collected during the selected window of time. A probability distribution function describing the observed waiting time between a randomly chosen instant and receiving a packet on the selected input channel is also generated. The distance between the two generated probability distribution functions is computed. If the computed distance is greater than a predefined confidence level, then the two selected channels are deemed to be related. Otherwise, the selected channels are deemed to be unrelated. The activity model is further generated by comparing each input and output channel pair entering or leaving a particular computer. | 10-30-2008 |
20090076794 | ADDING PROTOTYPE INFORMATION INTO PROBABILISTIC MODELS - Mechanisms are disclosed for incorporating prototype information into probabilistic models for automated information processing, mining, and knowledge discovery. Examples of these models include Hidden Markov Models (HMMs), Latent Dirichlet Allocation (LDA) models, and the like. The prototype information injects prior knowledge to such models, thereby rendering them more accurate, effective, and efficient. For instance, in the context of automated word labeling, additional knowledge is encoded into the models by providing a small set of prototypical words for each possible label. The net result is that words in a given corpus are labeled and are therefore in condition to be summarized, identified, classified, clustered, and the like. | 03-19-2009 |
20090144034 | TIME MODULATED GENERATIVE PROBABILISTIC MODELS FOR AUTOMATED CAUSAL DISCOVERY - Dependencies between different channels or different services in a client or server may be determined from the observation of the times of the incoming and outgoing of the packets constituting those channels or services. A probabilistic model may be used to formally characterize these dependencies. The probabilistic model may be used to list the dependencies between input packets and output packets of various channels or services, and may be used to establish the expected strength of the causal relationship between the different events surrounding those channels or services. Parameters of the probabilistic model may be either based on prior knowledge, or may be fit using statistical techniques based on observations about the times of the events of interest. Expected times of occurrence between events may be observed, and dependencies may be determined in accordance with the probabilistic model. | 06-04-2009 |
20100115216 | DATA ALLOCATION AND REPLICATION ACROSS DISTRIBUTED STORAGE SYSTEM - In a distributed storage system such as those in a data center or web based service, user characteristics and characteristics of the hardware such as storage size and storage throughput impact the capacity and performance of the system. In such systems, an allocation is a mapping from the user to the physical storage devices where data/information pertaining to the user will be stored. Policies regarding quality of service and reliability including replication of user data/information may be provided by the entity managing the system. A policy may define an objective function which quantifies the value of a given allocation. Maximizing the value of the allocation will optimize the objective function. This optimization may include the dynamics in terms of changes in patterns of user characteristics and the cost of moving data/information between the physical devices to satisfy a particular allocation. | 05-06-2010 |
20100241903 | AUTOMATED HEALTH MODEL GENERATION AND REFINEMENT - The present invention extends to methods, systems, and computer program products for automatically generating and refining health models. Embodiments of the invention use machine learning tools to analyze historical telemetry data from a server deployment. The tools output fingerprints, for example, small groupings of specific metrics-plus-behavioral parameters, that uniquely identify and describe past problem events mined from the historical data. Embodiments automatically translate the fingerprints into health models that can be directly applied to monitoring the running system. Fully-automated feedback loops for identifying past problems and giving advance notice as those problems emerge in the future is facilitated without any operator intervention. In some embodiments, a single portion of expert knowledge, for example, Key Performance Indicator (KPI) data, initiates health model generation. Once initiated, the feedback loop can be fully automated to access further telemetry and refine health models based on the further telemetry. | 09-23-2010 |
20100306597 | AUTOMATED IDENTIFICATION OF PERFORMANCE CRISIS - Methods for automatically identifying and classifying a crisis state occurring in a system having a plurality of computer resources. Signals are received from a device that collects the signals from each computer resource in the system. For each epoch, an epoch fingerprint is generated. Upon detecting a performance crisis within the system, a crisis fingerprint is generated consisting of at least one epoch fingerprint. The technology is able to identify that a performance crisis has previously occurred within the datacenter if a generated crisis fingerprint favorably matches any of the model crisis fingerprints stored in a database. The technology may also predict that a crisis is about to occur. | 12-02-2010 |
20110113004 | TIME MODULATED GENERATIVE PROBABILISTIC MODELS FOR AUTOMATED CAUSAL DISCOVERY USING A CONTINUOUS TIME NOISY-OR (CT-NOR) MODELS - Dependencies between different channels or different services in a client or server may be determined from the observation of the times of the incoming and outgoing of the packets constituting those channels or services. A probabilistic model may be used to formally characterize these dependencies. The probabilistic model may be used to list the dependencies between input packets and output packets of various channels or services, and may be used to establish the expected strength of the causal relationship between the different events surrounding those channels or services. Parameters of the probabilistic model may be either based on prior knowledge, or may be fit using statistical techniques based on observations about the times of the events of interest. Expected times of occurrence between events may be observed, and dependencies may be determined in accordance with the probabilistic model. | 05-12-2011 |
20110209001 | TIME MODULATED GENERATIVE PROBABILISTIC MODELS FOR AUTOMATED CAUSAL DISCOVERY - Dependencies between different channels or different services in a client or server may be determined from the observation of the times of the incoming and outgoing of the packets constituting those channels or services. A probabilistic model may be used to formally characterize these dependencies. The probabilistic model may be used to list the dependencies between input packets and output packets of various channels or services, and may be used to establish the expected strength of the causal relationship between the different events surrounding those channels or services. Parameters of the probabilistic model may be either based on prior knowledge, or may be fit using statistical techniques based on observations about the times of the events of interest. Expected times of occurrence between events may be observed, and dependencies may be determined in accordance with the probabilistic model. | 08-25-2011 |
20120072769 | REPAIR-POLICY REFINEMENT IN DISTRIBUTED SYSTEMS - In a distributed system a plurality of devices (including computing units, storage and communication units) are monitored by an automated repair service that uses sensors and performs one or more repair actions on computing devices that are found to fail according to repair policies. The repair actions include automated repair actions and non-automated repair actions. The health of the computing devices is recorded in the form of states along with the repair actions that were performed on the computing devices and the times at which the repair actions were performed, and events generated by both sensors and the devices themselves. After some period of the time, the history of states of each device, the events, and the repair actions performed on the computing devices are analyzed to determine the effectiveness of the repair actions. A statistical analysis is performed based on the cost of each repair action and the determined effectiveness of each repair action, and one or more of the policies may be adjusted, as well as determining from the signals and events from the sensors whether the sensors themselves require adjustment | 03-22-2012 |
20130124885 | ESTIMATING AND MANAGING POWER CONSUMPTION OF COMPUTING DEVICES USING POWER MODELS - Power consumption of computing devices are monitored with performance counters and used to generate a power model for each computing device. The power models are used to estimate the power consumption of each computing device based on the performance counters. Each computing device is assigned a power cap, and a software-based power control at each computing device monitors the performance counters, estimates the power consumption using the performance counters and the model, and compares the estimated power consumption with the power cap. Depending on whether the estimated power consumption violates the power cap, the power control may transition the computing device to a lower power state to prevent a violation of the power cap or a higher power state if the computing device is below the power cap. | 05-16-2013 |
20140214936 | IDENTIFYING SUBGRAPHS IN TRANSFORMED SOCIAL NETWORK GRAPHS - A graph of a social network is received. The graph may include a node for each user account and an edge between nodes that represent social networking relationships such as messages between the user accounts or a friend relationship. The graph is transformed into a transformed graph where nodes have direct edges depending on a local test among its neighbors in the original graph. Small subsets of the transformed graph are categorized. The categories are used to identify subgraphs in the transformed graph. Each subgraph is grown by adding an edge from the transformed graph to the subgraph depending on local tests among nodes associated with the edge that have at least one edge that is already in the subgraph. The categorized subgraphs are used to provide targeted advertising, suggest new connections, identify different personalities and interests of users, or to provide other services. | 07-31-2014 |